Principal Scientist – Protein MIL

Commonwealth Sciences, Inc.; Boston (MA); On-site; USD 180,000 - 240,000; Full time

Job summary

Commonwealth Sciences, Inc. is seeking a senior leader to drive computational biology and ML efforts for protein discovery, engineering, and characterization. You will develop deep learning models for structure prediction and protein design, and guide experiments with predictive insights.

You will build scalable pipelines for antibody discovery, protein data analytics, and deploy MLOps practices; mentor a small team while communicating results to diverse audiences.

Qualifications

• PhD in computational biology, bioinformatics, biophysics, ML, or a related field with protein science experience.
• 5+ years in industry or research with technical leadership or mentoring experience.
• Strong Python skills and hands-on PyTorch experience; ML models for protein structure and design.
• Experience with multimodal datasets and de novo protein design and validation.

Responsibilities

• Lead computational biology and ML efforts supporting protein discovery, engineering, and characterization.
• Develop, fine-tune, and benchmark deep learning and foundation models for structure prediction and design.
• Build predictive models for protein properties including stability, aggregation, expression, binding affinity.
• Design and execute computational campaigns and integrate results with experimental validation cycles.
• Develop scalable pipelines for antibody discovery, protein characterization, sequencing, and proteomics.
• Build data platforms, databases, APIs, and visualization tools for complex biological data.
• Collaborate with experimental scientists to design studies and analyze results.
• Manage cloud, GPU, and HPC environments; lead a small team of computational scientists.
• Establish best practices for data management, reproducibility, version control, and MLOps.
• Communicate strategies to technical and non-technical audiences and contribute to publications.

Skills

Python

PyTorch

Deep learning

Machine learning

Protein design

Leadership

Biophysics knowledge

Education

PhD in computational biology

Tools

AWS

Linux

GPU computing

Job description

• Lead computational biology and machine learning efforts supporting protein discovery, engineering, and characterization.
• Develop, fine-tune, and benchmark deep learning and foundation models for protein structure prediction, protein design, and antibody/binder discovery.
• Build predictive models for protein properties including stability, aggregation, expression, binding affinity, and developability.
• Design and execute computational protein and antibody design campaigns and integrate results with experimental validation cycles.
• Develop scalable computational pipelines for antibody discovery, protein characterization, sequencing, proteomics, and other biological datasets.
• Build data platforms, databases, APIs, and visualization tools that enable researchers to efficiently access and utilize complex biological data.
• Collaborate with experimental scientists to design studies, analyze results, and integrate computational approaches into laboratory workflows.
• Manage cloud, GPU, and high-performance computing environments supporting machine learning and large-scale biological analysis.
• Lead, mentor, and develop a small team of computational biologists and bioinformaticians.
• Establish best practices for data management, reproducibility, version control, model deployment, and MLOps.
• Communicate computational strategies and findings to both technical and non-technical audiences and contribute to publications, intellectual property, and product development.

Requirements

• PhD in computational biology, bioinformatics, biophysics, machine learning, or a related field, with strong experience in protein science or biochemistry.
• 5+ years of relevant industry or research experience, including technical leadership or experience mentoring/managing computational scientists.
• Strong Python programming skills and hands-on experience with deep learning frameworks such as PyTorch.
• Demonstrated experience developing, modifying, training, or applying machine learning models for protein structure prediction and/or protein design.
• Experience working with multimodal biological datasets, including sequence, structure, assay, sequencing, or proteomics data.
• Proven experience with de novo protein, binder, or antibody design and experimental validation.
• Strong understanding of computational protein science and the ability to translate research methods into practical tools and workflows.